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Healthcare providers usually record detailed notes of the clinical caredelivered to each patient for clinical, research, and billing purposes. Due tothe unstructured nature of these narratives, providers employ dedicated staffto assign diagnostic codes to patients' diagnoses using the InternationalClassification of Diseases (ICD) coding system. This manual process is not onlytime-consuming but also costly and error-prone. Prior work demonstratedpotential utility of Machine Learning (ML) methodology in automating thisprocess, but it has relied on large quantities of manually labeled data totrain the models. Additionally, diagnostic coding systems evolve with time,which makes traditional supervised learning strategies unable to generalizebeyond local applications. In this work, we introduce a generalweakly-supervised text classification framework that learns from class-labeldescriptions only, without the need to use any human-labeled documents. Itleverages the linguistic domain knowledge stored within pre-trained languagemodels and the data programming framework to assign code labels to individualtexts. We demonstrate the efficacy and flexibility of our method by comparingit to state-of-the-art weak text classifiers across four real-world textclassification datasets, in addition to assigning ICD codes to medical notes inthe publicly available MIMIC-III database.

Classifying Unstructured Clinical Notes via Automatic Weak Supervision